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Artificial Intelligence and Its Application in Robotics

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 22177

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Dr. Seong Hyeon Hong

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Department of Mechanical and Civil Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA
Interests: intelligent perception and control; path planning; prognostics and health management; machine learning (physics-informed learning)
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yi Wang

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Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
Interests: multidisciplinary analysis and optimization; large-scale system-level simulation; adaptive model integration; machine learning
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Special Issue Information

Dear Colleagues,

Intelligent robots are critical in various domains such as personal service, medical support, smart manufacturing, transportation, military applications, smart farming, unmanned exploration, and various industrial applications. To facilitate and advance the technological developments in intelligent systems, the prestigious journal of Applied Sciences invites you to propose novel research in the areas of artificial intelligence and robotics. This Special Issue seeks research dedicated to artificial intelligence applied across various fields of robotics, including but not limited to advanced perception (computer vision), intelligent control, reinforcement learning, meta-learning, human–robot interaction, human–machine interfaces, unmanned and autonomous systems, multi-robot systems, path planning, mapping, innovative sensor systems, field robotics, industrial robotics, medical robotics, and service robotics.

Dr. Seong Hyeon Hong
Prof. Dr. Yi Wang
Guest Editors

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Keywords

machine learning
machine vision
robotic control systems
human–robot interaction
innovative sensors
unmanned vehicles
field/industrial robotics

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Related Special Issue

Artificial Intelligence and Its Application in Robotics, 2nd Edition in Applied Sciences

Published Papers (10 papers)

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Research

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18 pages, 9481 KiB

Open AccessArticle

Miyazaki Vermin Repulsion Robot and Its Adjustable Acousto-Optic Stimulus Generation Scheme

by Geunho Lee, Teruyuki Yamane, Tasuku Koga and Tota Kuga

Appl. Sci. 2024, 14(19), 8955; https://doi.org/10.3390/app14198955 - 4 Oct 2024

Viewed by 6036

Abstract

One of the most pressing issues in livestock farming is the protection of economically valuable livestock. The prevention and the treatment of infectious diseases are directly related to maintaining stable livestock output. Vermin is a primary source of livestock infection, resulting in the occurrence and expansion of epidemic diseases. To protect livestock against infections caused by epidemic diseases, this study proposes a vermin repulsion system called the Miyazaki Vermin Repulsion Robot (MiVeReR). Different from existing vermin repulsion systems, the development objective of MiVeReR is to repel vermin rather than kill them. In particular, MiVeReR generates changeable acousto-optic signals as repulsion signals for wild animals. Furthermore, MiVeReR employs image data to monitor the invasion of wild animals and their location data to track them, and accurately focuses the generated signals on them. These acousto-optic stimuli can be changed based on the reactions of the intruder through the feedback of the image data to ensure the effectiveness of the repulsion motions for vermin. Details on the hardware configuration of MiVeReR and its control scheme are explained. As a first step to develop MiVeReR, we attempted to repel vermin such as mice and wild cats from farm environments. Extensive experiments were conducted to verify the effectiveness of MiVeReR and the proposed control solution. Through experiments in wild environments, the feasibility of MiVeReR was inspected. The results of this study are concretely described. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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17 pages, 1232 KiB

Open AccessArticle

Dual-Layer Reinforcement Learning for Quadruped Robot Locomotion and Speed Control in Complex Environments

by Yilin Zhang, Jiayu Zeng, Huimin Sun, Honglin Sun and Kenji Hashimoto

Appl. Sci. 2024, 14(19), 8697; https://doi.org/10.3390/app14198697 - 26 Sep 2024

Cited by 2 | Viewed by 3307

Abstract

Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot’s state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot’s perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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21 pages, 5473 KiB

Open AccessArticle

Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting

by Khalil Zbiss, Amal Kacem, Mario Santillo and Alireza Mohammadi

Appl. Sci. 2024, 14(19), 8614; https://doi.org/10.3390/app14198614 - 24 Sep 2024

Viewed by 1286

Abstract

This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution’s effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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18 pages, 3707 KiB

Open AccessArticle

Design of Minimal Model-Free Control Structure for Fast Trajectory Tracking of Robotic Arms

by Baptiste Toussaint and Maxime Raison

Appl. Sci. 2024, 14(18), 8405; https://doi.org/10.3390/app14188405 - 18 Sep 2024

Cited by 1 | Viewed by 1207

Abstract

This paper designs a minimal neural network (NN)-based model-free control structure for the fast, accurate trajectory tracking of robotic arms, crucial for large movements, velocities, and accelerations. Trajectory tracking requires an accurate dynamic model or aggressive feedback. However, such models are hard to obtain due to nonlinearities and uncertainties, especially in low-cost, 3D-printed robotic arms. A recently proposed model-free architecture has used an NN for the dynamic compensation of a proportional derivative controller, but the minimal requirements and optimal conditions remain unclear, leading to overly complex architectures. This study aims to identify these requirements and design a minimal NN-based model-free control structure for trajectory tracking. Two architectures are compared, one NN per joint (INN) and one global NN (GNN), each tested on two serial robotic arms in simulations and real scenarios. The results show that the architecture reduces tracking errors (RMSE < 2°). The INN is accurate for decoupled joint dynamics and requires fewer training data than the GNN. A table summarizes the design process. Future works will apply this control structure to low-cost robotic arms and micro-movements. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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19 pages, 1829 KiB

Open AccessArticle

Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping

by Huimin Sun, Yilin Zhang, Honglin Sun and Kenji Hashimoto

Appl. Sci. 2024, 14(17), 8009; https://doi.org/10.3390/app14178009 - 7 Sep 2024

Viewed by 1246

Abstract

Estimating the 6D pose and size of objects is crucial in the task of visual grasping for robotic arms. Most current algorithms still require the 3D CAD model of the target object to match with the detected points, and they are unable to predict the object’s size, which significantly limits the generalizability of these methods. In this paper, we introduce category priors and extract high-dimensional abstract features from both the observed point cloud and the prior to predict the deformation matrix of the reconstructed point cloud and the dense correspondence between the reconstructed and observed point clouds. Furthermore, we propose a staged geometric correction and dense correspondence refinement mechanism to enhance the accuracy of regression. In addition, a novel lightweight attention module is introduced to further integrate the extracted features and identify potential correlations between the observed point cloud and the category prior. Ultimately, the object’s translation, rotation, and size are obtained by mapping the reconstructed point cloud to a normalized canonical coordinate system. Through extensive experiments, we demonstrate that our algorithm outperforms existing methods in terms of performance and accuracy on commonly used benchmarks for this type of problem. Additionally, we implement the algorithm in robotic arm-grasping simulations, further validating its effectiveness. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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21 pages, 4861 KiB

Open AccessArticle

Surveillance Unmanned Ground Vehicle Path Planning with Path Smoothing and Vehicle Breakdown Recovery

by Tyler Parsons, Farhad Baghyari, Jaho Seo, Byeongjin Kim, Mingeuk Kim and Hanmin Lee

Appl. Sci. 2024, 14(16), 7266; https://doi.org/10.3390/app14167266 - 19 Aug 2024

Cited by 1 | Viewed by 1307

Abstract

As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds–Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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15 pages, 3294 KiB

Open AccessArticle

Implementation of a Small-Sized Mobile Robot with Road Detection, Sign Recognition, and Obstacle Avoidance

by Ching-Chang Wong, Kun-Duo Weng, Bo-Yun Yu and Yung-Shan Chou

Appl. Sci. 2024, 14(15), 6836; https://doi.org/10.3390/app14156836 - 5 Aug 2024

Viewed by 1905

Abstract

In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to let the robot have good computing processing and graphics processing capabilities. In addition, three functions of road detection, sign recognition, and obstacle avoidance are implemented on this small-sized robot. For road detection, we divide the captured image into four areas and use Intel NUC to perform road detection calculations. The proposed method can significantly reduce the system load and also has a high processing speed of 25 frames per second (fps). For sign recognition, we use the YOLOv4-tiny model and a data augmentation strategy to significantly improve the computing performance of this model. From the experimental results, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. For obstacle avoidance, a 2D LiDAR-based method with a distance-based filtering mechanism is proposed. The distance-based filtering mechanism is proposed to filter important data points and assign appropriate weights, which can effectively reduce the computational complexity and improve the robot’s response speed to avoid obstacles. Some results and actual experiments illustrate that the proposed methods for these three functions can be effectively completed in the implemented small-sized robot. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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14 pages, 687 KiB

Open AccessArticle

U-TFF: A U-Net-Based Anomaly Detection Framework for Robotic Manipulator Energy Consumption Auditing Using Fast Fourier Transform

by Ge Song, Seong Hyeon Hong, Tristan Kyzer and Yi Wang

Appl. Sci. 2024, 14(14), 6202; https://doi.org/10.3390/app14146202 - 17 Jul 2024

Viewed by 1263

Abstract

Robotic manipulators play a key role in modern industrial manufacturing processes. Monitoring their operational health is of paramount importance. In this paper, a novel anomaly detection framework named U-TFF is introduced for energy consumption auditing of robotic manipulators. It comprises a cascade of Time–Frequency Fusion (TFF) blocks to extract both time and frequency domain features from time series data. The block applies the Fast Fourier Transform to convert the input to the frequency domain, followed by two separate dense layers to process the resulting real and imaginary components, respectively. The frequency and time features are then combined to reconstruct the input. A U-shaped architecture is implemented to link corresponding TFF blocks of the encoder and decoder at the same level through skip connections. The semi-supervised model is trained using data exclusively from normal operations. Significant errors were generated during testing for anomalies with data distributions deviating from the training samples. Consequently, a threshold based on the magnitude of reconstruction errors was implemented to identify anomalies. Experimental validation was conducted using a custom dataset, including physical attacks as abnormal cases. The proposed framework achieved an accuracy and recall of approximately 0.93 and 0.83, respectively. A comparison with other benchmark models further verified its superior performance. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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27 pages, 3642 KiB

Open AccessArticle

Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model

by Przemyslaw Herman

Appl. Sci. 2024, 14(13), 5376; https://doi.org/10.3390/app14135376 - 21 Jun 2024

Cited by 1 | Viewed by 984

Abstract

This paper addresses the issue of trajectory tracking control for an autonomous underwater vehicle in the presence of parameter perturbations and disturbances in three-dimensional space. The control scheme is based on a combination of the backstepping method, the adaptive integral sliding mode control scheme, and velocity transformation resulting from the decomposition of the inertia matrix, which is symmetric. In addition, adaptive laws were applied to eliminate the effects of parameter perturbations and external disturbances. The main feature of the proposed approach is that the vehicle model is not fully symmetric but contains quantities due to the shift of the center of mass. Another important feature of the control scheme is the ability to detect some of the consequences caused by reducing the vehicle model by neglecting dynamic couplings. Numerical results on the five degrees of freedom (DOF) vehicle model show the efficiency, effectiveness, and robustness of the developed controller. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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Review

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39 pages, 4211 KiB

Open AccessReview

Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics

by Mohammed Aljamal, Sarosh Patel and Ausif Mahmood

Appl. Sci. 2025, 15(4), 1840; https://doi.org/10.3390/app15041840 - 11 Feb 2025

Viewed by 1970

Abstract

Common challenges in the area of robotics include issues such as sensor modeling, dynamic operating environments, and limited on-broad computational resources. To improve decision making, robots need a dependable framework to facilitate communication between different modules and the optimal action for real-world applications. The Robotics Operating System (ROS) and Reinforcement Learning (RL) are two promising approaches that help accomplish precise control, seamless integration of sensors-actuators, and exhibit learned behavior. The ROS enables seamless communication between heterogeneous components, while RL focuses on learning optimal behaviors through trial-and-error scenarios. Combining the ROS and RL offers superior decision making, improved perception, enhanced automation, and reliability. This work focuses on investigating ROS-based RL applications across various domains, aiming to enhance understanding through comprehensive discussion, analysis, and summarization. We base our evaluation on the application area, type of RL algorithm used, and degree of ROS–RL integration. Additionally, we provide summary of seminal works that define the current state of the art, along with GitHub repositories and resources for research purposes. Based on the review of successfully implemented projects, we make recommendations highlighting the advantages and limitations of RL techniques for specific applications and environments. The ultimate goal of this work is to advance the robotics field by providing a comprehensive overview of the recent important works that incorporate both the ROS and RL, thereby improving the adaptability of these emerging techniques. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)

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